The goal of lrstat is to calculate power and sample size under non-proportional hazards using weighted log-rank tests both analytically and using simulations in a time-to-event group-sequential trial.

It has built-in capabilities to use error-spending functions and can calcualte power, accrual duration, follow-up duration, and absolute accrual rates for the Fleming-Harringtonâ€™s class of weighting functions.

This is a basic example which shows you how to solve a common problem:

```
library(lrstat)
## basic example code for a two-stage group sequential trial with interim
## analysis at 80% of total number of events using Lan-DeMets O'Brien-Fleming
## error-spending. The accrual has a ramp-up periof of 9 months before
## reaching 26 patients per month. The survival distribution for the treatment
## group has a delay effect of 6 months and a hazard ratio 0.58 after the delay.
## The annual dropout rate is 5%. The accrual duration is 22 months.
## The follow-up duration is 18 months for the last randomized patients.
## The FH(0,1) weighted log-rank test is used for power calculation.
lrpower(kMax = 2, informationRates = c(0.8, 1),
alpha = 0.025, typeAlphaSpending = "sfOF",
allocationRatioPlanned = 1, accrualTime = seq(0, 9),
accrualIntensity = c(26/9*seq(1, 9), 26),
piecewiseSurvivalTime = c(0, 6),
lambda1 = c(0.0533, 0.0309),
lambda2 = c(0.0533, 0.0533),
gamma1 = -log(1-0.05)/12,
gamma2 = -log(1-0.05)/12,
accrualDuration = 22,
followupTime = 18, fixedFollowup = FALSE,
rho1 = 0, rho2 = 1,
numSubintervals = 2000)
```